Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act.
Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.
Campedelli, G. M., ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM, 2020-02-10T00:01:00Z [https://hdl.handle.net/10807/286563]
ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM
Campedelli, Gian Maria
2020
Abstract
Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act.File | Dimensione | Formato | |
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